隨機時序決策與分析
Sequential Decision Modeling and Analytics
| 節 | 週五 |
|---|---|
2 09:00–09:50 | 隨機時序決策與分析 MB506 3 節連堂 |
3 10:10–11:00 | |
4 11:10–12:00 |
* 根據陽明交大上課時間表所列
This course provides an in-depth exploration of sequential decision making (SDM) and its industrial applications. Students will learn to model SDM problems in a canonical mathematical form, apply fundamental algorithms such as dynamic programming and reinforcement learning in Python, and implement the framework in real-world applications. With homework assignments, in-class coding exercises, and a term project, students will gain both theoretical understanding and practical skills to address optimization, automation, and decision-making challenges under uncertainty.
Basic probability theory, Operations Research
Python-language programming
• Homework Assignments: 30% • Midterm Exam: 30% • Project: 40% • Participation: 5%
- Markov Decision Process (MDP)
- Reinforcement Learning (RL)
- Preliminary
| 週次 | 主題 |
|---|---|
| 第 1 週 | Introduction to Sequential Decision Making and Analytics |
| 第 2 週 | Preliminary - Basic Probability, Conditional Probabilities |
| 第 3 週 | Preliminary - Markov Chain Properties |
| 第 4 週 | MDP - Sequential Decision Modeling |
| 第 5 週 | MDP - Final Horizon MDP |
| 第 6 週 | Holiday: Double 10th Day |
| 第 7 週 | MDP - Infinite Horizon MDP |
| 第 8 週 | Holiday |
| 第 9 週 | Midterm Exam |
| 第 10 週 | MDP - Infinite Horizon MDP |
| 第 11 週 | RL - Introduction to model-free method |
| 第 12 週 | RL - Monte Carlo Method |
| 第 13 週 | RL - Temporal Difference (TD) Learning |
| 第 14 週 | RL - TD Learning |
| 第 15 週 | RL - Advanced Topics |
| 第 16 週 | Final Project Presentation |
• Ross, Sheldon M. (2014). Introduction to probability models. Academic press. • Warren B. Powell (2022). Reinforcement Learning and Stochastic Optimization: A unified framework for sequential decisions, John Wiley and Sons, Hoboken (free online) • Puterman, M. L. (2014). Markov decision processes: discrete stochastic dynamic programming. John Wiley & Sons. • Sutton R. & Barto A. (2020). Reinforcement Learning: An Introduction (2nd Edition). Cambridge: The MIT Press. (free online)
- 地點
- MB512
- 時間
- Mon. 12:00 – 14:00 (or by appointment)
- 聯絡方式
- kaiwen.tien@nycu.edu.tw